Pharmacometrics modeling and simulation to assist phenobarbital dose optimization in dogs.

Abstract

INTRODUCTION: Phenobarbital (PB) remains the first-line treatment for canine epilepsy due to its efficacy, affordability, and favorable pharmacokinetics. However, its narrow therapeutic index and substantial interindividual variability necessitate therapeutic drug monitoring (TDM) and individualized dosing. This study aimed to develop and validate a population pharmacokinetic (popPK) model of PB in dogs to support model-informed precision dosing (MIPD) in clinical practice. METHODS: A total of 121 serum samples from 100 dogs receiving PB monotherapy at steady state were used to build the model. An external dataset comprising 53 samples from 50 dogs was used for validation. Modeling was performed using nonlinear mixed-effects (NLME) techniques in MonolixSuite 2023R1. Covariate analysis included age, sex, and body weight (WT). Model performance was assessed using goodness-of-fit plots, prediction-corrected visual predictive checks (pcVPC), and calculation of mean error (ME), mean relative error (MRE), and root mean square error (RMSE). Monte Carlo simulations were conducted to evaluate the probability of target attainment (PTA) under different dosing regimens. RESULTS: A one-compartment model with autoinductive clearance (CL) best described PB pharmacokinetics. WT and age were significant covariates on apparent clearance (CL/F). The model accurately predicted PB concentrations in the external dataset (ME = -0.08 mg/L, MRE = 0.04%, RMSE = 2.04%). Simulations identified optimal dosing regimens stratified by age and WT, including recommendations for loading doses to accelerate attainment of therapeutic concentrations. DISCUSSION: The validated popPK model enables individualized PB dosing in dogs, accounting for variability in WT and age. This approach supports the implementation of MIPD in veterinary practice and may improve therapeutic outcomes while minimizing toxicity.